scholarly journals The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks

2021 ◽  
Author(s):  
Stavros I. Dimitriadis ◽  
Eirini Messaritaki ◽  
Derek Jones
Keyword(s):  
Community Detection ◽  
Brain Networks ◽  
Detection Algorithm ◽  
Construction Scheme ◽  
Community Detection Algorithm ◽  
The Impact ◽  
Structural Brain

scholarly journals The Impact of Graph Construction Scheme and Community Detection Algorithm on the Reliability of Community and Hub Identification in Structural Brain Networks

2020 ◽  
Author(s):  
Stavros I. Dimitriadis ◽  
Eirini Messaritaki ◽  
Derek K. Jones
Keyword(s):  
White Matter ◽  
Community Detection ◽  
Brain Networks ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Construction Scheme ◽  
Human Connectome Project ◽  
Community Detection Algorithm ◽  
The Impact ◽  
The Brain

AbstractThe human brain is a complex network of volumes of tissue (nodes) that are interconnected by white matter tracts (edges). It can be represented as a graph to allow us to use graph theory to gain insight into normal human development and brain disorders. Most graph theoretical metrics measure either whole-network (global) or node-specific (local) properties of the network. However, a critical question in network neuroscience is how nodes cluster together to form communities, each of which possibly plays a specific functional role. Community partition analysis allows us to investigate the mesoscale organization of the brain. Various algorithms have been proposed in the literature, that allow the identification of such communities, with each algorithm resulting in different communities for the network. Those communities also depend on the method used to weigh the edges of the graphs representing the brain networks. In this work, we use the test-retest data from the Human Connectome Project to compare 32 such community detection algorithms, each paired with 7 graph construction schemes, and assess the reproducibility of the resulting community partitions.The reproducibility of community partition depended heavily on both the graph construction scheme and the community detection algorithm. Hard community detection algorithms, via which each node is assigned to only one community, outperformed soft ones, via which each node can be a part of more than one community. The best reproducibility was observed for the graph construction scheme that combines 9 white matter tract metrics paired with the greedy stability optimization algorithm, with either discrete or continuous Markovian chain. This graph-construction scheme / community detection algorithm pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs were better reproduced than provincial hubs.

scholarly journals A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Dongqing Zhou ◽  
Xing Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Detection Problem ◽  
Signed Networks ◽  
Discrete Pso ◽  
Community Detection Algorithm ◽  
Definition Of ◽  
The Impact

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

scholarly journals Enhanced Community Detection Based on Cross Time for Higher Visibility in Supply Chain: A Six-Steps Model Framework

2019 ◽  
Vol 9 (1) ◽  
pp. 4509-4513
Keyword(s):  
Supply Chain ◽  
Community Detection ◽  
Detection Algorithm ◽  
Internal Quality ◽  
Supply Chain Network ◽  
Model Framework ◽  
Graph Analytics ◽  
Community Detection Algorithm ◽  
The Impact ◽  
The Relationship

Increasing the visibility in supply chain network had decrease the risk in industries. However, the current Cross-Time approach for temporal community detection algorithm in the visibility has fix number of communities and lack of operation such as split or merge. Therefore, improving temporal community detection algorithm to represent the relationship in supply chain network for higher visibility is significant. This paper proposed six steps model framework that aim: (1) To construct the nodes and vertices for temporal graph representing the relationship in supply chain network; (2) To propose an enhanced temporal community detection algorithm in graph analytics based on Cross-time approach and (3) To evaluate the enhanced temporal community detection algorithm in graph analytics for representing relationship in supply chain network based on external and internal quality analysis. The proposed framework utilizes the Cross-Time approach for enhancing temporal community detection algorithm. The expected result shows that the Enhanced Temporal Community Detection Algorithm based on Cross Time approach for higher visibility in supply chain network is the major finding when implementing this proposed framework. The impact advances industrialization through efficient supply chain in industry leading to urbanization.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

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